The present application generally relates to a system and method for performing analysis of events that appear in live and recorded video feeds, such as sporting events. In particular, the present application relates to a system and methods for enabling spatiotemporal analysis of component attributes and elements that make up events within a video feed, such as of a sporting event, systems for discovering, learning, extracting, and analyzing such events, metrics and analytic results relating to such events, and methods and systems for display, visualization, and interaction with outputs from such methods and systems.
Live events, such as sports, especially at the college and professional levels, continue to grow in popularity and revenue as individual colleges and franchises reap billions in revenue each year. To provide valuable insights and gain a competitive advantage in such endeavors, quantitative methodologies, such as Sabermetrics, have grown in importance and ubiquity as a valuable augmentation to traditional scouting methods. However, as no one person can evaluate and accurately store all of the information available from the vast volumes of sporting information generated on a daily basis, there seldom exists a storehouse of properly coded and stored information reflecting such large volumes of sports information and, even were such information available, there is lacking the provision of tools capable of mining and analyzing such information.
Systems are now available for capturing and encoding event information, such as sporting event information, such as “X, Y, Z” motion data captured by imaging cameras deployed in National Basketball Association (NBA) arenas. However, there are many challenges with such systems, including difficulty handling the data, difficulty transforming X, Y, Z data into meaningful and existing sports terminology, difficulty identifying meaningful insights from the data, difficulty visualizing results, and others. Also, there are opportunities to identify and extract novel insights from the data. Accordingly, a need exists for methods and systems that can take event data captured in video feeds and enable discovery and presentation of relevant events, metrics, analytic results, and insights.